Digital chemical pathology

Falahkheirkhah, Kianoush

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https://hdl.handle.net/2142/120292 Copy

Description

Title

Digital chemical pathology

Author(s)

Falahkheirkhah, Kianoush

Issue Date

2023-04-24

Director of Research (if dissertation) or Advisor (if thesis)

Bhargava, Rohit

Doctoral Committee Chair(s)

Zhao, Huimin

Committee Member(s)

• Rao, Christopher V
• Harley, Brendan A

Department of Study

Chemical & Biomolecular Engr

Discipline

Chemical Engineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Chemical imaging
• Digital pathology
• Machine learning
• Computer vision

Abstract

The field of pathology has relied on traditional morphological examination for decades, which can be time-consuming and costly for low-resource institutions. However, recent advancements in digital pathology and machine learning have shown promise in streamlining the process and improving healthcare outcomes for patients. In this thesis, we introduce a new technique called "Digital Chemical Pathology" (DCP), which integrates label-free imaging methods like chemical imaging with innovative machine learning techniques to measure and analyze both the morphology and chemistry of pathology samples. By being sensitive to chemical properties and considering morphology, DCP aims to provide a more comprehensive molecular analysis of tissue and aid in diagnosis and prognosis. As a result of DCP, we expect to alter the current workflow of pathology, making it faster, more accurate, and more accessible to a wider range of patients, ultimately enhancing healthcare for everyone.

Graduation Semester

2023-05

Type of Resource

Thesis

Copyright and License Information

Copyright 2023 Kianoush Falahkheirkhah

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